Paper on Hazardous Behavior

Research Article
Effect of High-Altitude Environment on Driving Safety: A Study on
Drivers’ Mental Workload, Situation Awareness, and
Driving Behaviour

Xinyan Wang,1 Wu Bo ,1,2 Weihua Yang,1 Suping Cui,1 and Pengzi Chu3

1School of Engineering, Tibet University, Lhasa 850000, China
2School of Transportation, Southeast University, Nanjing 211189, China
3%e Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, Shanghai 201804, China

Correspondence should be addressed to Wu Bo; [email protected]

Received 27 September 2019; Revised 22 June 2020; Accepted 3 July 2020; Published 21 July 2020

Academic Editor: Maria Castro

Copyright © 2020 Xinyan Wang et al. *is is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

*is study aims to analyze the effect of high-altitude environment on drivers’ mental workload (MW), situation awareness (SA),
and driving behaviour (DB), and to explore the relationship among those driving performances. Based on a survey, the data of 356
lowlanders engaging in driving activities at Tibetan Plateau (high-altitude group) and 341 lowlanders engaging in driving activities
at low altitudes (low-altitude group) were compared and analyzed. *e results suggest that the differences between the two groups
are noteworthy. Mental workload of high-altitude group is significantly higher than that of low-altitude group, and their situation
awareness is lower significantly. *e possibility of risky driving behaviours for high-altitude group, especially aggressive vio-
lations, is higher. For the high-altitude group, the increase of mental workload can lead to an increase on aggressive violations, and
the situation understanding plays a full mediating effect between mental workload and aggressive violations. Measures aiming at
the improvement of situation awareness and the reduction of mental workload can effectively reduce the driving risk from high-
altitude environment for lowlanders.

1. Introduction

Road traffic injury is now the leading cause of death, par-
ticularly for persons aged 5–29 years [1]. Road safety is an
important public health concern around the world, and safe
mobility has been considered as a human right [2]. Scholars
have long been committed to the reduction of traffic
accidents.

Tibetan Plateau is an oxygen-deprived region with an
average altitude of more than 4 000 m above sea level [3],
which is the first step of China’s terrain [4]. Physical activity
at high altitude for lowlanders can induce acute mountain
sickness (AMS) [5, 6], and even diseases, such as hyper-
tension [7]. *e status of traffic safety in the region also
needs to be improved. According to the statistics of the
National Bureau of of China, for Tibet, there were
363 traffic accidents, 124 death tolls, and 2.43 million yuan’s

property damage caused by traffic accidents in 2018.
However, the region’s permanent population at the end of
the year was only 3.44 million, indicating the road safety
condition was also not optimistic. However, there are many
floating populations from low altitudes taking driving ac-
tivity, a kind of physical work, in Tibet.

From an intuitive perspective, the high-altitude envi-
ronment plays a negative impact on driving activities. But,
there are few studies focused on the suitability of driving
activities for drivers at high altitude. Previous studies had
confirmed that the physical work capacity of low-altitude
residents (i.e., lowlanders) was significantly reduced at high
altitudes [8]. An experiment on the Qinghai-Tibet plateau
showed that the higher the altitude, the more fatigue the
driver [9]. When driving at high altitude, the mental
workload of drivers would heighten with the increase of
altitude, together with the increase of fatigue, reaction time,

Hindawi
Journal of Advanced Transportation
Volume 2020, Article ID 7283025, 10 pages
https://doi.org/10.1155/2020/7283025

mailto:[email protected]

https://orcid.org/0000-0001-8604-3711

https://creativecommons.org/licenses/by/4.0/

https://doi.org/10.1155/2020/7283025

and emotional stress [10]. On the other hand, the conclu-
sions obtained from short-term stress reaction had not
considered the long-term adaptability of human sufficiently,
and the results may not be applicable to the safety features of
drivers at high altitude for a long time. Based on this, in
order to discuss the safety status for floating drivers, the
study explored the performance and influencing factors
based on questionnaire data. Indicators selected included
mental workload, situation awareness, and driving
behaviour.

Mental workload, situation awareness, and driving be-
haviour are important factors influencing driver safety. For
driver, mental workload can be defined as the proportion of
information processing capability used to perform a driving
task [11]. Mental workload that is too high or too low is not
conducive to driving safety [12, 13]. As an assessment index
to analyze drivers’ performance, situation awareness is a very
important precondition to drive safely in a complex and
dynamic environment. It can be described as the ability to
accurately perceive the traffic environment for drivers and to
adapt their interaction with distracting activities [14, 15].
Drivers with higher situation awareness can find more
hazards in a driving task [16]. For driving behaviour, it is
widely analyzed for the possibility of being involved in a
traffic accident and can be regarded as a series of driver’s
behaviours while driving [17–19].

Being able to measure with questionnaires is one other
reason to choose these indicators (i.e., mental workload,
situation awareness, and driving behaviour) in the study. For
example, the driver behaviour questionnaire (DBQ) pro-
posed by Reason [18] has been widely extended and applied
to survey drivers’ self-reported driving behaviours [20, 21].
*e Situation Awareness Global Assessment Technique
(SAGAT) [22] and the Situation Awareness Rating Tech-
nique (SART) [23] have been widely used for the mea-
surement of situation awareness [24–26]. *e Subjective
Work Assessment Technique (SWAT) and the National
Aeronautics and Space Administration-Task Load Index
(NASA-TLX) are popular measuring tools of mental
workload [27–31].

*is study focuses on different performances between
drivers at high altitudes from low altitudes and drivers at low
altitudes. Specifically, the study is organized as follows: some
analyses are implemented in Section 2 (study 1) for the
differences of drivers’ mental workload, situation awareness,
and driving behaviour. In Section 3 (study 2), the rela-
tionships among the three factors are analyzed based on the
structural equation modeling (SEM). *en, Section 4 and
Section 5 are the discussion and the conclusion of the study,
respectively.

2. Study1:DifferencesonDrivingPerformances

Considering the undesired influences of high-altitude en-
vironment on human’s physiological condition [6, 8, 10], the
study aims at determining if there are adverse effects of high-
altitude environment on driving safety to drivers from low
altitudes.

2.1. Methodology

2.1.1. Design. In fact, factors affecting driver’s safety are
varied, and a proper assumption need to be set before
statistical analysis. For this, the study first assumed that there
was no significant difference in their normal driving tasks
between the drivers at high altitudes from low altitudes
(high-altitude group) and drivers at low altitudes (low-al-
titude group). Driving tasks involved of these two groups are
their common driving activities, and the standards of traffic
management, traffic design, and traffic regulations are highly
consistent in the two areas. *erefore, the assumption that
driving activities of these two groups are similar is
reasonable.

For the differences of the two groups on driving per-
formances, a survey on driving performances was carried out
from three angles: mental workload, situation awareness,
and driving behaviour, and the analysis of variance
(ANOVA) was used to the comparison. Meanwhile, there
are three hypotheses need to be tested:

H1: mental workload of high-altitude group is signif-
icantly higher than that of the low-altitude group
H2: situation awareness of high-altitude group is sig-
nificantly lower than that of the low-altitude group
H3: undesired driving behaviour of high-altitude group
is significantly more frequent than that of the low-al-
titude group

2.1.2. Materials and Procedure. *e subjective workload
assessment technique (SWAT) with equal weight was chosen
as the measuring tool, which has a desired sensitivity
[31, 32]. During the preparation of the questionnaire, three
questions of SWAT [27] were modified to suit driving task
(e.g., “how high is your stress usually when driving on Ti-
betan Plateau?”), and a preresearch had been implemented
to reduce the difficulty of understanding. Answers to those
questions were designed to utilize a three-point scale. *e
SWAT contains three dimensions: time load (TL), mental
effort load (EL), and psychological stress load (SL). *e score
of mental workload (MW) in the study was calculated by the
following formula [32]:

MW �
TL + EL + SL

3
. (1)

For situation awareness, the situation awareness global
assessment technique (SAGAT) and the situation awareness
rating technique (SART) are popular measuring tools for
situation awareness [24–26]. SAGAT was commonly used in
the process of an experiment [33], and SART was often used
for post hoc evaluation [34]. Clearly, SART is more suitable
for the study.

During the preparation of the questionnaire, the ques-
tions of 10-D SART [23] were modified to suit the driving
task (e.g., “how high is your alertness usually when driving
on Tibetan Plateau?”), and a preresearch had been com-
pleted to improve readability. Answers to these questions
were designed to utilize a ten-point scale. *e 10-D SART

2 Journal of Advanced Transportation

contains ten questions which could be further grouped into
three overall dimensions named 3D-SART: (a) attention
demand (AD); (b) attention supply (AS); and (c) situation
understanding (SU). Specifically, attention demand is a
combination of the instability of situation, the complexity of
situation, and the variability of situation; attention supply is
a combination of the arousal of situation, the concentration
of attention, the division of attention, and the spare mental
capacity; situation understanding is the combination of the
quantity of information, the quality of information, and the
degree of familiarity. By using a group score, the score of SA
was calculated by the following formula [24]:

SA � SU − (AD − AS), (2)

where SU is the situation understanding, AD is the attention
demand, and AS is the attention supply.

In terms of measuring tool of driving behaviour, the
driver behaviour questionnaire (DBQ) is an instrument
applied widely to examine the self-reported driving be-
haviour [18, 20, 21]. *e DBQ with three-factor structure
(e.g., errors, lapses, and violations) or four-factor structure
(e.g., errors, lapses, ordinary violations, and aggressive vi-
olations) has been broadly implemented in many studies
[35–37]. In the study, a DBQ considering errors, lapses,
ordinary violations, and aggressive violations and including
23 items was carried out to gather data. All the items were
derived or revised from literatures of af Wåhlberg et al. [38],
Bener et al. [39], Liu and Chen [3], Reason et al. [18],
Hezaveh et al. [40], and Maslać et al. [41]. Every question has
five options by using a five-point scale ranging from never
(1) to nearly all the time (5). Meanwhile, Chinese statements
of the referenced items were translated and back translated
to minimize the difficulty of understanding on the premise
of ensuring the original meaning.

A five-month survey was carried out to obtain ques-
tionnaire data, and the survey was conducted in the form of
electronic questionnaire and distributed by social media
such as email, QQ, and WeChat. Participants were invited to
participate in the survey with a certain charge. And, 1295
copies of questionnaire were obtained. 356 participants were
lowlanders from low altitudes, that is, provinces in the third
step of China’s terrain with an average altitude of less than
500 m above sea level [4], and the lowlanders had engaged in
driving task on Tibetan Plateau (high-altitude group). Other
939 participants were also from these low altitudes.

*e platform of electronic questionnaire can automat-
ically identify the city where the participant was located and
judge whether it was a valid object according to holding a
valid driver license or not, the identified city, and the filled
place where the households are registered. Only valid par-
ticipants can complete the questionnaire. *e lowlanders of
939 participants conducted the self-reports of mental
workload, situation awareness, and driving behaviour
according to their experience, and the high-altitude group
reported their experience of driving at high altitude
according to the content of questionnaire. On the other
hand, to reduce the difference between these two groups on
demographic characteristics, the collection of high-altitude

group’s data was finished first. And, to meet the charac-
teristics of the high-altitude group, a total of 939 copies were
collected, of which 341 copies were selected randomly as a
control group (low-altitude group).

*e analytical approach involved in the study contains
reliability analysis, validity analysis, and differential analysis.
Cronbach’s alpha, factor load matrix, the statistic of Kai-
ser–Meyer–Olkin (KMO) test, and Bartlett’s spherical test
were used to further identify reliability or validity [42]. As is
mentioned above, for the comparison on mental workload,
situation awareness and driving behaviour between high-
altitude group and low-altitude group, the analysis of var-
iance (ANOVA), which has been used widely for the dif-
ferential analysis on driver performances was selected
[40, 43].

2.2. Results. *e summary of those 697 participants is given
in Table 1. *e results of analysis of variance (ANOVA)
indicate that there is no significant difference between the
two groups on traits of gender, age, years of driving expe-
rience, and driving distance. *at is to say, the control group
is effective. *en, the analysis in the study is based on these
data.

*e reliability analysis showed that Cronbach’s alpha of
the subjective workload assessment technique (SWAT) was
0.641. Typically, Cronbach’s alpha greater than 0.7 is ideal
[42], and the value is lower than that. *e result may be the
cause that the set option of SWAT is a three-point scale, and
SWAT only contains three dimensions. Based on this, the
result had been accepted in the study and SWAT could be
utilized as a tool for drivers to measure mental workload.

Further, the results of analysis of variance indicate that
the p value for time workload (TL) was 0.141 (F (1, 695) �
3.549, p>0.05), the p values for mental effort load (EL)
equaled 0.000 (F (1, 695) � 44.149, p<0.01), psychological stress load (SL) equaled 0.000 (F (1, 695) � 24.587, p<0.01), and mental workload (MW) 0.000 (F (1, 695) � 35.207, p<0.01). *erefore, there are strong evidences of difference between drivers of the high-altitude group and drivers of the low-altitude group on EL, SL, and MW, and those indicators of the high-altitude group are higher than those of the low- altitude group (Figure 1). *e hypothesis of H1 is valid and acceptable. Table 2 shows the reliability of the situation awareness rating technique (SART) in different dimensions and the statistics of 10 items. *e results show that Cronbach’s alpha of SARTand its three dimensions are all greater than 0.7, and the reliability is ideal. *e difference test results showed that the p values of attention demand (AD), attention supply (AS), and situation understanding (SU) equaled 0.004 (F (1, 695) � 8.235, p<0.01), 0.000 (F (1, 695) � 13.104, p<0.01), and 0.000 (F (1, 695) � 64.697, p<0.01), respectively. And, the p value of SA was 0.000 (F (1, 695) � 15.880, p<0.01). *us, there are strong evidences of difference between drivers of the high- altitude group and drivers of the low-altitude group on attention demand, attention supply, situation understand- ing, and situation awareness, with lower on attention supply, Journal of Advanced Transportation 3 situation understanding, and situation awareness but higher on attention demand of the high-altitude group (Figure 2). And, the hypothesis of H2 is also acceptable. Results of reliability analysis in Table 3 show that the values of Cronbach’s alpha are greater than 0.7, which in- dicate the internal consistency of the driver behaviour questionnaire (DBQ) is ideal. For the validity, because each item comes from researches related to driving behaviour in the past, the content validity is ideal. In terms of structural validity verified by factor analysis, four components were retained with eigenvalues greater than 1, and the rotating component matrix is shown in Table 4. Due to the existence of cross-loading of ov_2 (increase speed to pass a yellow light) and ov_5 (disregard the speed limit of the roads), with 0.654 and 0.654 to ordinary violations but 0.401 and 0.411 to aggressive violations, these two items were removed. As shown in Table 3, before and after ov_2 and ov_5 was de- leted, the values of Cronbach’s alpha and KMO statistics and results of Bartlett’s spherical test are in the ideal range. *e cumulative proportion of variance contribution of these four factors increase from 59.150 to 60.300 and from 48.261 to 50.876 to ordinary violations. Results of analysis of variance showed that the p values of ordinary violations (OV), errors (ER), aggressive Table 1: Sample information. Categorical variable (F (1, 695), p value) Category High-altitude group (N � 356) Low-altitude group (N � 341) Gender (1.469, 0.226) Female 74 84 Male 282 257 Age (0.060, 0.807) Up to 30 years 240 235 Above 30 years 116 106 Years of driving experience (0.800, 0.372) Up to 5 years 272 281 Above 5 years 84 60 Driving distance (3.504, 0.062) Up to 50,000 km 257 255 Above 50,000 km 99 86 Years of driving experience on Tibetan Plateau Up to 1 years 201 — Above 1 years 155 — Driving distance on Tibetan Plateau Up to 10,000 km 206 — Above 10,000 km 150 — 0 1 2 3 TL EL SL MW Sc or es High altitude Low altitude Figure 1: Scores of mental workload. Table 2: Results of internal consistency and statistics. Dimensions (Cronbach’s alpha) Items (notation) High-altitude group Low-altitude group Mean (std. D) Mean (std. D) SA (0.856) AD (0.869) Instability of situation (s11) 6.247 (2.102) 5.589 (1.981) Complexity of situation (s12) 6.169 (2.225) 5.868 (1.947) Variability of situation (s13) 6.225 (2.206) 5.997 (1.903) AS (0.806) Arousal of situation (s21) 6.534 (2.071) 7.114 (1.807) Division of attention (s22) 6.301 (2.026) 7.399 (1.797) Spare mental capacity (s23) 6.284 (1.978) 6.557 (1.698) Concentration of attention (s24) 6.927 (1.768) 6.328 (1.843 SU (0.771) Information quantity (s31) 5.596 (2.404) 6.689 (1.658) Information quality (s32) 6.239 (1.839) 6.645 (1.742) Familiarity (s33) 6.208 (1.996) 6.786 (1.806) 4 Journal of Advanced Transportation violations (AV), and lapses (LA) equaled 0.076 (F (1, 695) � 3.148, p>0.05), 0.432 (F (1, 695) � 0.619, p>0.05), 0.000 (F
(1, 695) � 23.147, p<0.01), and 0.198 (F (1, 695) � 1.662, p>0.05), respectively. And, the p value of the total score of
driving behaviours (DB) was 0.030 (F (1, 695) � 4.741,
p<0.05). Hence, there are strong evidences of difference AD AS SU SA 10 8 6 4 2 0 Sc or es High altitude Low altitude Figure 2: Scores of situation awareness. Table 3: Results of internal consistency and validity of factors. Category Cronbach’s alpha KMO statistic Bartlett’s spherical test Cumulative (%) Ordinary violations (OV) 0.731 (0.811)a 0.744 (0.839)a 0.000 (0.000)a 50.876 (48.261)a Errors (ER) 0.864 0.862 0.000 64.717 Aggressive violations (AV) 0.821 0.824 0.000 59.127 Lapses (LA) 0.845 0.856 0.000 61.694 Driving behaviours (DB) 0.917 (0.921)a 0.927 (0.927)a 0.000 (0.000)a 60.300 (59.150)a aResult before ov_2 and ov_5 was deleted. Table 4: Factor loading and statistics. Category Brief items High- altitude Low-altitude Factor loading Mean (SD) Mean (SD) Ordinary violations (OV) ov_1 Ignore the red light and pass through an intersection 1.447 (0.794) 1.420 (0.643) 0.701 ov_2a Increase speed to pass a yellow light 2.101 (1.119) 1.971 (0.781) 0.654 (0.401) ov_3 Drive the wrong lane in the opposite direction 1.320 (0.699) 1.325 (0.533) 0.736 ov_4 Take more passengers than allowed 1.253 (0.674) 1.299 (0.561) 0.648 ov_5a Disregard the speed limit of the roads 1.843 (1.068) 1.736 (0.787) 0.654 (0.411) ov_6 Forget to wear seat belt 1.694 (1.074) 1.472 (0.731) 0.534 ov_7 Use the cellular phone while driving 1.975 (1.041) 1.823 (0.863) 0.479 Errors (ER) er_1 Fail to notice when a traffic-signal turns green 2.039 (0.972) 2.009 (0.705) 0.675 er_2 Misjudge an overtaking gap 1.879 (0.981) 1.942 (0.753) 0.787 er_3 Hit a cyclist nearly when turning right 1.767 (0.958) 1.806 (0.755) 0.658 er_4 Brake inappropriately to stop 1.826 (0.972) 1.959 (0.795) 0.789 er_5 Insufficient attention to vehicle or pedestrian ahead 1.803 (0.962) 1.832 (0.720) 0.620 Aggressive violations (AV) av_1 Drive too close to impel the car in front to go faster 1.927 (1.018) 1.710 (0.733) 0.557 av_2 Feel angered by another driver’s behaviour 2.360 (1.106) 1.925 (0.883) 0.744 av_3 Become impatient with a slow driver and pass on the right 2.421 (1.156) 2.238 (0.922) 0.670 av_4 Race away from traffic lights to beat the driver next to you 1.801 (0.980) 1.545 (0.702) 0.58 av_5 Be annoyed and sound the horn 1.896 (1.017) 1.725 (0.787) 0.592 Lapses (LA) la_1 Intend to A, but driving on route to B 2.410 (0.982) 2.493 (0.789) 0.696 la_2 Turn on the wrong device of the vehicle 1.935 (1.006) 1.919 (0.770) 0.710 la_3 Forget where the car parked 1.924 (1.014) 2.006 (0.892) 0.732 la_4 Feel unsure about the lane when approaching an intersection 2.017 (1.045) 1.954 (0.868) 0.724 la_5 Forget to open lights timely when the night has come 2.110 (1.049) 1.870 (0.805) 0.688 aVariable was dropped from the measurement due to cross-loading, with 0.401 and 0.411 to aggressive violations, respectively. Journal of Advanced Transportation 5 between the high-altitude group and the low-altitude group on driving behaviours, with more undesired risky driving behaviour for the high-altitude group. Meanwhile, accord- ing to the results, the difference is mainly caused by the behaviour of aggressive violations with smaller p values similarly and simultaneously (Figure 3). *e results partly support the hypothesis of H3. 3. Study 2: Factors Affecting Drivers’ Aggressive Violations Considering the significant difference on aggressive viola- tions between the two groups, the causes of the phenomenon are worth exploring. Do the level of mental workload and situation or situation awareness affect the frequency of aggressive violations for high-altitude group? And, is there a progressive relationship between the three dimensions of situation awareness? *e analysis may lead to some impli- cations for the management of aggressive violations for the group. 3.1. Methodology 3.1.1. Design. Aiming at the relationships between the factors of mental workload, situation awareness, and ag- gressive violations for the high-altitude group, the sample of high-altitude group was applied to statistical analysis based on the method of structural equation modeling (SEM). For the verification, the following six hypotheses need to be further tested (Figure 4): H41: attention demand has a significant positive impact on attention supply H42: attention supply has a significant positive impact on situation understanding H43: attention demand has a significant positive impact on mental workload H44: mental workload has a significant negative impact on situation understanding H45: situation understanding has a significant negative impact on aggressive violations H46: mental workload has a significant positive impact on aggressive violations 3.1.2. Statistical Analysis. In order to verify the roadmap or model above, a structural equation model was established to develop a path analysis using maximum likelihood for the multidimensional relationships between drivers’ aggressive violations, mental workload, attention demand, attention supply, and situation understanding. While the structural equation model was developed, the goodness-of-fit of the model was assessed according to CMIN/DF, absolute index (including GFI, AGFI, and RMSEA), incremental index (including NFI and CFI), and parsimony index (including PGFI and PNFI), following the recommendations of several literatures [17, 44, 45]. *e recommended threshold of CMIN/DF was less than 0.3, of GFI, AGFI, NFI, and CFI more than 0.9, of RMSEA less than 0.08 or 0.05, and of PGFI and PNFI more than 0.5 [17, 44, 46]. In the study, to acquire a better goodness-of-fit, the original model was modified according to the modification indices (MIs) [17, 47]. As for sampling of SEM, several recommendations suggested that sample size should be at least 10–15 times the number of observed variables [45, 48]. In this study, data from high-altitude group were used, and the sample size was 19.778 times the number of observed variables (356 samples/ 18 observed variables). *e analysis tool involved in the study was AMOS 21.0 version. 3.2.Results. *e original model followed the conception of Figure 4 and had been revised to improve the goodness- of-fit by correlating the error terms of e12 and e33, e22 and e33, and ea2 and ea4 because of larger modification indices (MIs). Regression weights between latent variables and observed variables and covariances and correlations between error terms and variances of the modified model (Figure 5) were all significant. In terms of goodness-of-fit, the results exported by AMOS were that chi-squared equaled 247.147, degree of freedom 126, CMIN/DF 1.961, GFI 0.904, AGFI 0.870, RMSEA 0.052, NFI 0.901, CFI 0.948, PGFI 0.745, and PNFI 0.742. *us, only AGFI is lower than the recommended thresholds, and the model fits the data well. Meanwhile, results of the path analysis (Table 5) supported some hypotheses. For the three dimensions of situation awareness, hypothesis H41 and hypothesis H42 were valid (p<0.001). *e increase of the attention 0 1 2 3 4 5 OV ER AV LA DB Sc or es High altitude Low altitude Figure 3: Scores of driving behaviour. Attention demand Mental workload Attention supply Situation understanding Aggressive violations H41 H42 H43 H44 H46 H45 Figure 4: Hypothesis roadmap. 6 Journal of Advanced Transportation demand could spur the increase of driver’s attention supply, and the increase of the attention supply led to an increase of the level of situation understanding. *e result showed a progressive relationship among attention de- mand, attention supply, and situation understanding, and the intermediary role of the attention supply was also valid. In addition, the increase of the attention demand could increase driver’s mental workload (p<0.001), but the increase of the mental workload did not mean an increase in the level of the situation understanding (p>0.05). For the group, the increase of the mental
workload could increase their aggressive violations
(p<0.05), and the increase of the level of the situation understanding could reduce the frequency of aggressive violations (p<0.05). *us, the situation understanding played a full mediating role between the mental workload and the aggressive violations. Moreover, the mental workload also played a mediating role between the at- tention demand and aggressive violations. 4. Discussion *e results of study 1 and study 2 support some hypotheses, including that the high-altitude group has more driving behaviors of aggressive violations, greater mental workload, and lower situation awareness than of the lower altitude group. And, aggressive violations are positively correlated with the mental workload and negatively correlated with the situation understanding. Some discussions of these results are as follows. Due to a higher mental effort load and psychological stress load, the mental workload of the high-altitude group is significantly higher than that of the low-altitude group. As mentioned above, many studies have confirmed that the physical work capacity of low-altitude residents is signifi- cantly reduced at high altitudes [8]. Drivers are more prone to fatigue with the increase of altitude [9]. *e raise of al- titude not only leads to an increase in mental workload but also affects the driver’s reaction time and their mood [10]. In AD e11 s11 e12 s12 e13 s13 AS e21 s21 e23 s23 e24 s24 SU e33 s33 e32 s32 e31 s31 e22 s22 MW em1SL em2EL em3TL AV ea2av_2 ea3av_3 ea4av_4 ea5av_5 ea1av_1 es1 es2 es3 ea em 0.36 0.53 –0.54 0.83 0.84 0.73 0.48 0.87 0.76 0.76 0.64 0.78 0.82 0.90 0.87 0.63 0.72 0.68 0.06 0.27 0.63 0.71 0.50 0.40